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- affiliation: IBM Almaden Research Center
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2020 – today
- 2025
[i80]Vitaly Feldman, Moshe Shenfeld:
Privacy amplification by random allocation. CoRR abs/2502.08202 (2025)- [i79]Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar:
Local Pan-Privacy for Federated Analytics. CoRR abs/2503.11850 (2025) - [i78]Hilal Asi, Vitaly Feldman, Hannah Keller, Guy N. Rothblum, Kunal Talwar:
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications. CoRR abs/2503.11897 (2025) - [i77]Hilal Asi, Vitaly Feldman, Hannah Keller, Guy N. Rothblum, Kunal Talwar:
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications. IACR Cryptol. ePrint Arch. 2025: 490 (2025) - 2024
- [c83]Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren:
Faster Convergence with MultiWay Preferences. AISTATS 2024: 433-441 - [c82]Kunal Talwar
, Shan Wang, Audra McMillan, Vitaly Feldman, Pansy Bansal, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Congzheng Song, Linmao Song, Karl Tarbe, Sebastian Vogt, Shundong Zhou, Vojta Jina, Michael Scaria, Luke Winstrom:
Samplable Anonymous Aggregation for Private Federated Data Analysis. CCS 2024: 2859-2873 - [c81]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou:
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages. ICML 2024 - [c80]Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar:
Instance-Optimal Private Density Estimation in the Wasserstein Distance. NeurIPS 2024 - [c79]Amrith Setlur, Vitaly Feldman, Kunal Talwar:
Private and Personalized Frequency Estimation in a Federated Setting. NeurIPS 2024 - [c78]Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar:
Differentially Private Heavy Hitter Detection using Federated Analytics. SaTML 2024: 512-533 - [i76]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou:
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages. CoRR abs/2404.10201 (2024) - [i75]Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar:
Instance-Optimal Private Density Estimation in the Wasserstein Distance. CoRR abs/2406.19566 (2024) - 2023
- [c77]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Online Prediction from Experts: Separations and Faster Rates. COLT 2023: 674-699 - [c76]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. ICML 2023: 1107-1120 - [c75]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar:
Fast Optimal Locally Private Mean Estimation via Random Projections. NeurIPS 2023 - [c74]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Stronger Privacy Amplification by Shuffling for Renyi and Approximate Differential Privacy. SODA 2023: 4966-4981 - [i74]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. CoRR abs/2302.14154 (2023) - [i73]Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar:
Fast Optimal Locally Private Mean Estimation via Random Projections. CoRR abs/2306.04444 (2023) - [i72]Karan N. Chadha, Junye Chen, John C. Duchi, Vitaly Feldman, Hanieh Hashemi, Omid Javidbakht, Audra McMillan, Kunal Talwar:
Differentially Private Heavy Hitter Detection using Federated Analytics. CoRR abs/2307.11749 (2023) - [i71]Kunal Talwar, Shan Wang, Audra McMillan, Vojta Jina, Vitaly Feldman, Bailey Basile, Áine Cahill, Yi Sheng Chan, Mike Chatzidakis, Junye Chen, Oliver R. A. Chick, Mona Chitnis, Suman Ganta, Yusuf Goren, Filip Granqvist, Kristine Guo, Frederic Jacobs, Omid Javidbakht, Albert Liu, Richard Low, Dan Mascenik, Steve Myers, David Park, Wonhee Park, Gianni Parsa, Tommy Pauly, Christian Priebe, Rehan Rishi, Guy N. Rothblum, Michael Scaria, Linmao Song, Congzheng Song, Karl Tarbe, Sebastian Vogt, Luke Winstrom, Shundong Zhou:
Samplable Anonymous Aggregation for Private Federated Data Analysis. CoRR abs/2307.15017 (2023) - [i70]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. CoRR abs/2307.15835 (2023) - [i69]Martin Pelikan, Sheikh Shams Azam, Vitaly Feldman, Jan Honza Silovsky, Kunal Talwar, Tatiana Likhomanenko:
Federated Learning with Differential Privacy for End-to-End Speech Recognition. CoRR abs/2310.00098 (2023) - [i68]Aadirupa Saha, Vitaly Feldman, Tomer Koren, Yishay Mansour:
Faster Convergence with Multiway Preferences. CoRR abs/2312.11788 (2023) - 2022
- [c73]Hilal Asi, Vitaly Feldman, Kunal Talwar:
Optimal Algorithms for Mean Estimation under Local Differential Privacy. ICML 2022: 1046-1056 - [c72]Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar:
Private frequency estimation via projective geometry. ICML 2022: 6418-6433 - [c71]Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar:
Mean Estimation with User-level Privacy under Data Heterogeneity. NeurIPS 2022 - [c70]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. NeurIPS 2022 - [i67]Vitaly Feldman, Jelani Nelson, Huy Le Nguyen, Kunal Talwar:
Private Frequency Estimation via Projective Geometry. CoRR abs/2203.00194 (2022) - [i66]Hilal Asi, Vitaly Feldman, Kunal Talwar:
Optimal Algorithms for Mean Estimation under Local Differential Privacy. CoRR abs/2205.02466 (2022) - [i65]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy. CoRR abs/2208.04591 (2022) - [i64]Nicholas Carlini, Vitaly Feldman, Milad Nasr:
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy". CoRR abs/2209.14987 (2022) - [i63]John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar:
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. CoRR abs/2210.13497 (2022) - [i62]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Online Prediction from Experts: Separations and Faster Rates. CoRR abs/2210.13537 (2022) - [i61]Audra McMillan, Omid Javidbakht, Kunal Talwar, Elliot Briggs, Mike Chatzidakis, Junye Chen, John C. Duchi, Vitaly Feldman, Yusuf Goren, Michael Hesse, Vojta Jina, Anil Katti, Albert Liu, Cheney Lyford, Joey Meyer, Alex Palmer, David Park, Wonhee Park, Gianni Parsa, Paul Pelzl, Rehan Rishi, Congzheng Song, Shan Wang, Shundong Zhou:
Private Federated Statistics in an Interactive Setting. CoRR abs/2211.10082 (2022) - 2021
- [j23]Yuval Dagan, Vitaly Feldman:
Interaction is Necessary for Distributed Learning with Privacy or Communication Constraints. J. Priv. Confidentiality 11(2) (2021) - [j22]Vitaly Feldman, Cristóbal Guzmán, Santosh Srinivas Vempala:
Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization. Math. Oper. Res. 46(3): 912-945 (2021) - [c69]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. FOCS 2021: 954-964 - [c68]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. ICML 2021: 393-403 - [c67]Vitaly Feldman, Kunal Talwar:
Lossless Compression of Efficient Private Local Randomizers. ICML 2021: 3208-3219 - [c66]Vitaly Feldman, Tijana Zrnic:
Individual Privacy Accounting via a Rényi Filter. NeurIPS 2021: 28080-28091 - [c65]Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar:
When is memorization of irrelevant training data necessary for high-accuracy learning? STOC 2021: 123-132 - [e3]Vitaly Feldman, Katrina Ligett, Sivan Sabato:
Algorithmic Learning Theory, 16-19 March 2021, Virtual Conference, Worldwide. Proceedings of Machine Learning Research 132, PMLR 2021 [contents] - [i60]Vitaly Feldman, Kunal Talwar:
Lossless Compression of Efficient Private Local Randomizers. CoRR abs/2102.12099 (2021) - [i59]Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in 𝓁1 Geometry. CoRR abs/2103.01516 (2021) - 2020
- [j21]Vitaly Feldman, Pravesh Kothari, Jan Vondrák:
Tight bounds on ℓ1 approximation and learning of self-bounding functions. Theor. Comput. Sci. 808: 86-98 (2020) - [c64]Yuval Dagan, Vitaly Feldman:
PAC learning with stable and private predictions. COLT 2020: 1389-1410 - [c63]Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar:
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. NeurIPS 2020 - [c62]Vitaly Feldman, Chiyuan Zhang:
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. NeurIPS 2020 - [c61]Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private stochastic convex optimization: optimal rates in linear time. STOC 2020: 439-449 - [c60]Yuval Dagan, Vitaly Feldman:
Interaction is necessary for distributed learning with privacy or communication constraints. STOC 2020: 450-462 - [c59]Vitaly Feldman:
Does learning require memorization? a short tale about a long tail. STOC 2020: 954-959 - [i58]Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta:
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation. CoRR abs/2001.03618 (2020) - [i57]Vitaly Feldman, Tomer Koren, Kunal Talwar:
Private Stochastic Convex Optimization: Optimal Rates in Linear Time. CoRR abs/2005.04763 (2020) - [i56]Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar:
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses. CoRR abs/2006.06914 (2020) - [i55]Vitaly Feldman, Chiyuan Zhang:
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. CoRR abs/2008.03703 (2020) - [i54]Vitaly Feldman, Tijana Zrnic:
Individual Privacy Accounting via a Renyi Filter. CoRR abs/2008.11193 (2020) - [i53]Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar:
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? CoRR abs/2012.06421 (2020) - [i52]Vitaly Feldman, Audra McMillan, Kunal Talwar:
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling. CoRR abs/2012.12803 (2020)
2010 – 2019
- 2019
- [c58]Vitaly Feldman, Jan Vondrák:
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate. COLT 2019: 1270-1279 - [c57]Amit Daniely, Vitaly Feldman:
Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? COLT 2019: 3180-3184 - [c56]Vitaly Feldman, Roy Frostig, Moritz Hardt:
Open Problem: How fast can a multiclass test set be overfit? COLT 2019: 3185-3189 - [c55]Vitaly Feldman, Roy Frostig, Moritz Hardt:
The advantages of multiple classes for reducing overfitting from test set reuse. ICML 2019: 1892-1900 - [c54]Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta:
Private Stochastic Convex Optimization with Optimal Rates. NeurIPS 2019: 11279-11288 - [c53]Amit Daniely, Vitaly Feldman:
Locally Private Learning without Interaction Requires Separation. NeurIPS 2019: 14975-14986 - [c52]Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta:
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. SODA 2019: 2468-2479 - [i51]Vitaly Feldman, Jan Vondrák:
High probability generalization bounds for uniformly stable algorithms with nearly optimal rate. CoRR abs/1902.10710 (2019) - [i50]Vitaly Feldman, Roy Frostig, Moritz Hardt:
The advantages of multiple classes for reducing overfitting from test set reuse. CoRR abs/1905.10360 (2019) - [i49]Vitaly Feldman:
Does Learning Require Memorization? A Short Tale about a Long Tail. CoRR abs/1906.05271 (2019) - [i48]Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Thakurta:
Private Stochastic Convex Optimization with Optimal Rates. CoRR abs/1908.09970 (2019) - [i47]Yuval Dagan, Vitaly Feldman:
Interaction is necessary for distributed learning with privacy or communication constraints. CoRR abs/1911.04014 (2019) - [i46]Yuval Dagan, Vitaly Feldman:
PAC learning with stable and private predictions. CoRR abs/1911.10541 (2019) - 2018
- [j20]Vitaly Feldman, Will Perkins, Santosh S. Vempala:
On the Complexity of Random Satisfiability Problems with Planted Solutions. SIAM J. Comput. 47(4): 1294-1338 (2018) - [c51]Vitaly Feldman, Thomas Steinke:
Calibrating Noise to Variance in Adaptive Data Analysis. COLT 2018: 535-544 - [c50]Cynthia Dwork, Vitaly Feldman:
Privacy-preserving Prediction. COLT 2018: 1693-1702 - [c49]Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta:
Privacy Amplification by Iteration. FOCS 2018: 521-532 - [c48]Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro:
The Everlasting Database: Statistical Validity at a Fair Price. NeurIPS 2018: 6532-6541 - [c47]Vitaly Feldman, Jan Vondrák:
Generalization Bounds for Uniformly Stable Algorithms. NeurIPS 2018: 9770-9780 - [i45]Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro:
The Everlasting Database: Statistical Validity at a Fair Price. CoRR abs/1803.04307 (2018) - [i44]Cynthia Dwork, Vitaly Feldman:
Privacy-preserving Prediction. CoRR abs/1803.10266 (2018) - [i43]Vitaly Feldman, Ilya Mironov, Kunal Talwar, Abhradeep Thakurta:
Privacy Amplification by Iteration. CoRR abs/1808.06651 (2018) - [i42]Amit Daniely, Vitaly Feldman:
Learning without Interaction Requires Separation. CoRR abs/1809.09165 (2018) - [i41]Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Abhradeep Thakurta:
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. CoRR abs/1811.12469 (2018) - [i40]Vitaly Feldman, Jan Vondrák:
Generalization Bounds for Uniformly Stable Algorithms. CoRR abs/1812.09859 (2018) - 2017
- [j19]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Guilt-free data reuse. Commun. ACM 60(4): 86-93 (2017) - [j18]Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh S. Vempala, Ying Xiao:
Statistical Algorithms and a Lower Bound for Detecting Planted Cliques. J. ACM 64(2): 8:1-8:37 (2017) - [c46]Vitaly Feldman, Pravesh Kothari, Jan Vondrák:
Tight Bounds on ℓ1 Approximation and Learning of Self-Bounding Functions. ALT 2017: 540-559 - [c45]Vitaly Feldman:
Dealing with Range Anxiety in Mean Estimation via Statistical Queries. ALT 2017: 629-640 - [c44]Vitaly Feldman, Thomas Steinke:
Generalization for Adaptively-chosen Estimators via Stable Median. COLT 2017: 728-757 - [c43]Vitaly Feldman:
A General Characterization of the Statistical Query Complexity. COLT 2017: 785-830 - [c42]Vitaly Feldman, Badih Ghazi:
On the Power of Learning from k-Wise Queries. ITCS 2017: 41:1-41:32 - [c41]Vitaly Feldman, Cristóbal Guzmán, Santosh S. Vempala:
Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization. SODA 2017: 1265-1277 - [i39]Vitaly Feldman, Badih Ghazi:
On the Power of Learning from k-Wise Queries. CoRR abs/1703.00066 (2017) - [i38]Vitaly Feldman, Thomas Steinke:
Generalization for Adaptively-chosen Estimators via Stable Median. CoRR abs/1706.05069 (2017) - [i37]Vitaly Feldman, Thomas Steinke:
Calibrating Noise to Variance in Adaptive Data Analysis. CoRR abs/1712.07196 (2017) - 2016
- [j17]Vitaly Feldman, Jan Vondrák:
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas. SIAM J. Comput. 45(3): 1129-1170 (2016) - [j16]Miklós Ajtai, Vitaly Feldman, Avinatan Hassidim, Jelani Nelson:
Sorting and Selection with Imprecise Comparisons. ACM Trans. Algorithms 12(2): 19:1-19:19 (2016) - [c40]Vitaly Feldman, Alexander Rakhlin:
Conference on Learning Theory 2016: Preface. COLT 2016: 1-3 - [c39]Vitaly Feldman:
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back. NIPS 2016: 3576-3584 - [e2]Vitaly Feldman, Alexander Rakhlin, Ohad Shamir:
Proceedings of the 29th Conference on Learning Theory, COLT 2016, New York, USA, June 23-26, 2016. JMLR Workshop and Conference Proceedings 49, JMLR.org 2016 [contents] - [r4]Vitaly Feldman:
Hardness of Proper Learning. Encyclopedia of Algorithms 2016: 897-900 - [r3]Vitaly Feldman:
Statistical Query Learning. Encyclopedia of Algorithms 2016: 2090-2095 - [i36]Vitaly Feldman:
A General Characterization of the Statistical Query Complexity. CoRR abs/1608.02198 (2016) - [i35]Vitaly Feldman:
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back. CoRR abs/1608.04414 (2016) - [i34]Vitaly Feldman:
Dealing with Range Anxiety in Mean Estimation via Statistical Queries. CoRR abs/1611.06475 (2016) - 2015
- [j15]Maria-Florina Balcan, Vitaly Feldman:
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy. Algorithmica 72(1): 282-315 (2015) - [j14]Vitaly Feldman, Pravesh Kothari:
Agnostic learning of disjunctions on symmetric distributions. J. Mach. Learn. Res. 16: 3455-3467 (2015) - [j13]Vitaly Feldman, David Xiao:
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity. SIAM J. Comput. 44(6): 1740-1764 (2015) - [c38]Vitaly Feldman, Jan Vondrák:
Tight Bounds on Low-Degree Spectral Concentration of Submodular and XOS Functions. FOCS 2015: 923-942 - [c37]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Generalization in Adaptive Data Analysis and Holdout Reuse. NIPS 2015: 2350-2358 - [c36]Vitaly Feldman, Will Perkins, Santosh S. Vempala:
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's. NIPS 2015: 2836-2844 - [c35]Dana Dachman-Soled, Vitaly Feldman, Li-Yang Tan, Andrew Wan, Karl Wimmer:
Approximate resilience, monotonicity, and the complexity of agnostic learning. SODA 2015: 498-511 - [c34]Vitaly Feldman, Will Perkins, Santosh S. Vempala:
On the Complexity of Random Satisfiability Problems with Planted Solutions. STOC 2015: 77-86 - [c33]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Leon Roth:
Preserving Statistical Validity in Adaptive Data Analysis. STOC 2015: 117-126 - [i33]Miklós Ajtai, Vitaly Feldman, Avinatan Hassidim, Jelani Nelson:
Sorting and Selection with Imprecise Comparisons. CoRR abs/1501.02911 (2015) - [i32]Vitaly Feldman, Jan Vondrák:
Tight Bounds on Low-degree Spectral Concentration of Submodular and XOS functions. CoRR abs/1504.03391 (2015) - [i31]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Generalization in Adaptive Data Analysis and Holdout Reuse. CoRR abs/1506.02629 (2015) - [i30]Vitaly Feldman, Cristóbal Guzmán, Santosh S. Vempala:
Statistical Query Algorithms for Stochastic Convex Optimization. CoRR abs/1512.09170 (2015) - 2014
- [j12]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio:
Nearly Optimal Solutions for the Chow Parameters Problem and Low-Weight Approximation of Halfspaces. J. ACM 61(2): 11:1-11:36 (2014) - [c32]Vitaly Feldman, Pravesh Kothari:
Learning Coverage Functions and Private Release of Marginals. COLT 2014: 679-702 - [c31]Vitaly Feldman, David Xiao:
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity. COLT 2014: 1000-1019 - [c30]Vitaly Feldman:
Open Problem: The Statistical Query Complexity of Learning Sparse Halfspaces. COLT 2014: 1283-1289 - [c29]Vitaly Feldman, Jan Vondrák:
Optimal bounds on approximation of submodular and XOS functions by juntas. ITA 2014: 1-10 - [e1]Maria-Florina Balcan, Vitaly Feldman, Csaba Szepesvári:
Proceedings of The 27th Conference on Learning Theory, COLT 2014, Barcelona, Spain, June 13-15, 2014. JMLR Workshop and Conference Proceedings 35, JMLR.org 2014 [contents] - [i29]Vitaly Feldman, David Xiao:
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity. CoRR abs/1402.6278 (2014) - [i28]Vitaly Feldman, Pravesh Kothari, Jan Vondrák:
Nearly Tight Bounds on ℓ1 Approximation of Self-Bounding Functions. CoRR abs/1404.4702 (2014) - [i27]Dana Dachman-Soled, Vitaly Feldman, Li-Yang Tan, Andrew Wan, Karl Wimmer:
Approximate resilience, monotonicity, and the complexity of agnostic learning. CoRR abs/1405.5268 (2014) - [i26]Vitaly Feldman, Pravesh Kothari:
Agnostic Learning of Disjunctions on Symmetric Distributions. CoRR abs/1405.6791 (2014) - [i25]Vitaly Feldman, Will Perkins, Santosh S. Vempala:
Subsampled Power Iteration: a New Algorithm for Block Models and Planted CSP's. CoRR abs/1407.2774 (2014) - [i24]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Preserving Statistical Validity in Adaptive Data Analysis. CoRR abs/1411.2664 (2014) - [i23]Vitaly Feldman, Will Perkins, Santosh S. Vempala:
On the Complexity of Random Satisfiability Problems with Planted Solutions. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [j11]Vitaly Feldman, Jan Vondrák:
Structure and learning of valuation functions. SIGecom Exch. 12(2): 50-53 (2013) - [c28]Pranjal Awasthi, Vitaly Feldman, Varun Kanade:
Learning Using Local Membership Queries. COLT 2013: 398-431 - [c27]Vitaly Feldman, Pravesh Kothari, Jan Vondrák:
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees. COLT 2013: 711-740 - [c26]Vitaly Feldman, Jan Vondrák:
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas. FOCS 2013: 227-236 - [c25]Andrew S. Cassidy, Paul Merolla, John V. Arthur, Steven K. Esser, Bryan L. Jackson, Rodrigo Alvarez-Icaza, Pallab Datta, Jun Sawada, Theodore M. Wong, Vitaly Feldman, Arnon Amir, Daniel Ben Dayan Rubin, Filipp Akopyan, Emmett McQuinn, William P. Risk, Dharmendra S. Modha:
Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores. IJCNN 2013: 1-10 - [c24]Maria-Florina Balcan, Vitaly Feldman:
Statistical Active Learning Algorithms. NIPS 2013: 1295-1303 - [c23]Sudeepa Roy, Laura Chiticariu, Vitaly Feldman, Frederick Reiss, Huaiyu Zhu:
Provenance-based dictionary refinement in information extraction. SIGMOD Conference 2013: 457-468 - [c22]Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh S. Vempala, Ying Xiao:
Statistical algorithms and a lower bound for detecting planted cliques. STOC 2013: 655-664 - [i22]Vitaly Feldman, Pravesh Kothari, Jan Vondrák:
Representation, Approximation and Learning of Submodular Functions Using Low-rank Decision Trees. CoRR abs/1304.0730 (2013) - [i21]Vitaly Feldman, Pravesh Kothari:
Learning Coverage Functions. CoRR abs/1304.2079 (2013) - [i20]Maria-Florina Balcan, Vitaly Feldman:
Statistical Active Learning Algorithms. CoRR abs/1307.3102 (2013) - [i19]Vitaly Feldman, Jan Vondrák:
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas. CoRR abs/1307.3301 (2013) - [i18]Vitaly Feldman, Will Perkins, Santosh S. Vempala:
On the Complexity of Random Satisfiability Problems with Planted Solutions. CoRR abs/1311.4821 (2013) - 2012
- [j10]Vitaly Feldman:
A complete characterization of statistical query learning with applications to evolvability. J. Comput. Syst. Sci. 78(5): 1444-1459 (2012) - [j9]Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, Yi Wu:
Agnostic Learning of Monomials by Halfspaces Is Hard. SIAM J. Comput. 41(6): 1558-1590 (2012) - [c21]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio:
Nearly optimal solutions for the chow parameters problem and low-weight approximation of halfspaces. STOC 2012: 729-746 - [c20]Vitaly Feldman, Varun Kanade:
Computational Bounds on Statistical Query Learning. COLT 2012: 16.1-16.22 - [c19]Vitaly Feldman:
Learning DNF Expressions from Fourier Spectrum. COLT 2012: 17.1-17.19 - [i17]Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh S. Vempala:
The Complexity of Statistical Algorithms. CoRR abs/1201.1214 (2012) - [i16]Vitaly Feldman:
Learning DNF Expressions from Fourier Spectrum. CoRR abs/1203.0594 (2012) - [i15]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio:
Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces. CoRR abs/1206.0985 (2012) - [i14]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio:
Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces. Electron. Colloquium Comput. Complex. TR12 (2012) - [i13]Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh S. Vempala, Ying Xiao:
Statistical Algorithms and a Lower Bound for Planted Clique. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [c18]Vitaly Feldman:
Distribution-Independent Evolvability of Linear Threshold Functions. COLT 2011: 253-272 - [c17]Vitaly Feldman, Homin K. Lee, Rocco A. Servedio:
Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas. COLT 2011: 273-292 - [i12]Vitaly Feldman:
Distribution-Independent Evolvability of Linear Threshold Functions. CoRR abs/1103.4904 (2011) - 2010
- [c16]Vitaly Feldman:
Distribution-Specific Agnostic Boosting. ICS 2010: 241-250 - [i11]Vitaly Feldman:
A Complete Characterization of Statistical Query Learning with Applications to Evolvability. CoRR abs/1002.3183 (2010) - [i10]Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, Yi Wu:
Agnostic Learning of Monomials by Halfspaces is Hard. CoRR abs/1012.0729 (2010) - [i9]Vitaly Feldman:
A Complete Characterization of Statistical Query Learning with Applications to Evolvability. Electron. Colloquium Comput. Complex. TR10 (2010) - [i8]Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, Yi Wu:
Agnostic Learning of Monomials by Halfspaces is Hard. Electron. Colloquium Comput. Complex. TR10 (2010) - [i7]Vitaly Feldman, Homin K. Lee, Rocco A. Servedio:
Lower Bounds and Hardness Amplification for Learning Shallow Monotone Formulas. Electron. Colloquium Comput. Complex. TR10 (2010)
2000 – 2009
- 2009
- [j8]Vitaly Feldman:
Hardness of approximate two-level logic minimization and PAC learning with membership queries. J. Comput. Syst. Sci. 75(1): 13-26 (2009) - [j7]Vitaly Feldman:
On The Power of Membership Queries in Agnostic Learning. J. Mach. Learn. Res. 10: 163-182 (2009) - [j6]Vitaly Feldman, Leslie G. Valiant:
Experience-Induced Neural Circuits That Achieve High Capacity. Neural Comput. 21(10): 2715-2754 (2009) - [j5]Vitaly Feldman, Parikshit Gopalan, Subhash Khot, Ashok Kumar Ponnuswami:
On Agnostic Learning of Parities, Monomials, and Halfspaces. SIAM J. Comput. 39(2): 606-645 (2009) - [j4]Vitaly Feldman, Shrenik Shah:
Separating models of learning with faulty teachers. Theor. Comput. Sci. 410(19): 1903-1912 (2009) - [c15]Vitaly Feldman:
Robustness of Evolvability. COLT 2009 - [c14]Vitaly Feldman:
A Complete Characterization of Statistical Query Learning with Applications to Evolvability. FOCS 2009: 375-384 - [c13]Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, Yi Wu:
Agnostic Learning of Monomials by Halfspaces Is Hard. FOCS 2009: 385-394 - [c12]Miklós Ajtai, Vitaly Feldman, Avinatan Hassidim, Jelani Nelson:
Sorting and Selection with Imprecise Comparisons. ICALP (1) 2009: 37-48 - [i6]Vitaly Feldman:
Distribution-Specific Agnostic Boosting. CoRR abs/0909.2927 (2009) - 2008
- [j3]Michael Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi:
The complexity of properly learning simple concept classes. J. Comput. Syst. Sci. 74(1): 16-34 (2008) - [c11]Vitaly Feldman:
On the Power of Membership Queries in Agnostic Learning. COLT 2008: 147-156 - [c10]Vitaly Feldman, Leslie G. Valiant:
The Learning Power of Evolution. COLT 2008: 513-514 - [c9]Vitaly Feldman:
Evolvability from learning algorithms. STOC 2008: 619-628 - [r2]Vitaly Feldman:
Hardness of Proper Learning. Encyclopedia of Algorithms 2008 - [r1]Vitaly Feldman:
Statistical Query Learning. Encyclopedia of Algorithms 2008 - [i5]Vitaly Feldman:
On The Power of Membership Queries in Agnostic Learning. Electron. Colloquium Comput. Complex. TR08 (2008) - 2007
- [j2]Vitaly Feldman:
Attribute-Efficient and Non-adaptive Learning of Parities and DNF Expressions. J. Mach. Learn. Res. 8: 1431-1460 (2007) - [c8]Vitaly Feldman, Shrenik Shah, Neal Wadhwa:
Separating Models of Learning with Faulty Teachers. ALT 2007: 94-106 - 2006
- [c7]Vitaly Feldman:
Optimal Hardness Results for Maximizing Agreements with Monomials. CCC 2006: 226-236 - [c6]Vitaly Feldman, Parikshit Gopalan, Subhash Khot, Ashok Kumar Ponnuswami:
New Results for Learning Noisy Parities and Halfspaces. FOCS 2006: 563-574 - [c5]Vitaly Feldman:
Hardness of approximate two-level logic minimization and PAC learning with membership queries. STOC 2006: 363-372 - [i4]Vitaly Feldman:
Optimal Hardness Results for Maximizing Agreements with Monomials. Electron. Colloquium Comput. Complex. TR06 (2006) - [i3]Vitaly Feldman:
On Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions. Electron. Colloquium Comput. Complex. TR06 (2006) - [i2]Vitaly Feldman, Parikshit Gopalan, Subhash Khot, Ashok Kumar Ponnuswami:
New Results for Learning Noisy Parities and Halfspaces. Electron. Colloquium Comput. Complex. TR06 (2006) - 2005
- [c4]Vitaly Feldman:
On Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions. COLT 2005: 576-590 - [i1]Vitaly Feldman:
Hardness of Approximate Two-level Logic Minimization and PAC Learning with Membership Queries. Electron. Colloquium Comput. Complex. TR05 (2005) - 2004
- [c3]Michael Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi:
Learnability and Automatizability. FOCS 2004: 621-630 - 2002
- [j1]Nader H. Bshouty, Vitaly Feldman:
On Using Extended Statistical Queries to Avoid Membership Queries. J. Mach. Learn. Res. 2: 359-395 (2002) - 2001
- [c2]Nader H. Bshouty, Vitaly Feldman:
On Using Extended Statistical Queries to Avoid Membership Queries. COLT/EuroCOLT 2001: 529-545 - 2000
- [c1]Ayal Zaks, Vitaly Feldman, Nava Aizikowitz:
Sealed calls in Java packages. OOPSLA 2000: 83-92
Coauthor Index
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